prompt stringlengths 15 655k | completion stringlengths 3 32.4k | api stringlengths 8 52 |
|---|---|---|
import sqlite3
import numpy as np
import Helpers
conn = sqlite3.connect('../data/SandP500.sqlite3')
all_tickers = Helpers.get_all_tickers(conn)
cursor = conn.cursor()
prices_at_start = np.array([])
prices_at_end = np.array([])
for ticker in all_tickers:
cursor.execute("SELECT closing_price "
... | np.append(prices_at_start, price_at_start) | numpy.append |
import numpy as np
from ..visualization import Viewer
from ..utils import Subject, Observer, deprecated, matrices, NList
import copy
from numba import njit, int64, float64
from numba.types import ListType as LT
@njit(int64[:](LT(LT(int64))), cache=True)
def _valence(adj_x2y):
valences = np.zeros(len(adj_x2y), dtyp... | np.logical_xor(flip_z,((centroids[:,2] >= min_z) & (centroids[:,2] <= max_z))) | numpy.logical_xor |
import os, math
import _pickle as pickle
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from sklearn import preprocessing
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--data-folder', default='data', help='Parent dir of the dataset')
parser.add_argument('--f... | np.zeros(shape=(test_n, input_len)) | numpy.zeros |
import numpy as np
import sys, os
if __name__== "__main__":
# read samples mesh gids
smgids = np.loadtxt("sample_mesh_gids.dat", dtype=int)
print(smgids)
# read full velo
fv = np.loadtxt("./full/velo.txt")
# read full velo
fullJ = np.loadtxt("./full/jacobian.txt")
# read sample mesh velo
sv = np... | np.allclose(maskedJacob.shape, sjac.shape) | numpy.allclose |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Module for tools used in vaspy
"""
import bz2
from itertools import zip_longest
import os
import re
import numpy as np
from typing import List, Iterable, Sequence, Tuple, Union, IO, Any, Optional
def open_by_suffix(filename: str) -> IO[str]:
"""Open file."""
... | np.array(crystal_axes[2]) | numpy.array |
__author__ = 'Mario'
import numpy as np
from scipy.stats import norm
class EuropeanLookback():
def __init__(self, strike, expiry, spot, sigma, rate, dividend, M, flag, N=100, Vbar=.12, alpha=.69):
# Instantiate variables
self.strike = float(strike)
self.expiry = float(expiry)
self... | np.sqrt(Vtn) | numpy.sqrt |
import unittest
from scipy.stats import gaussian_kde
from scipy.linalg import cholesky
import numpy as np
from pyapprox.bayesian_inference.laplace import *
from pyapprox.density import NormalDensity, ObsDataDensity
from pyapprox.utilities import get_low_rank_matrix
from pyapprox.randomized_svd import randomized_svd, Ma... | np.dot(gradient,directions) | numpy.dot |
"""Class for playing and annotating video sources in Python using Tkinter."""
import json
import logging
import pathlib
import datetime
import tkinter
import tkinter.filedialog
import numpy as np
import cv2
import PIL.Image
import PIL.ImageTk
logger = logging.getLogger("VideoPyer")
logging.basicConfig(level=logging.I... | np.array([x1, y1]) | numpy.array |
from DNN.hans_on_feedforward_neural_network import Feedforward_neural_network
import numpy as np
Net = Feedforward_neural_network()
#--------------------------多元回归实验-----------------------------
# ---------------------------准备数据-------------------------------
#--------------------------------------------------------... | np.random.normal(0, 10, size=Y_data.shape) | numpy.random.normal |
#Contains MeldCohort and MeldSubject classes
from contextlib import contextmanager
from meld_classifier.paths import (
DEMOGRAPHIC_FEATURES_FILE,
CORTEX_LABEL_FILE,
SURFACE_FILE,
DEFAULT_HDF5_FILE_ROOT,
BOUNDARY_ZONE_FILE,
NVERT,
BASE_PATH,
)
import pandas as pd
import numpy as np
import ni... | np.sum(self.cohort.surf_area[lesion]) | numpy.sum |
import numpy as np
import math
import os
def load_obj(dire):
fin = open(dire,'r')
lines = fin.readlines()
fin.close()
vertices = []
triangles = []
for i in range(len(lines)):
line = lines[i].split()
if len(line)==0:
continue
if line[0] == 'v':
... | np.array(triangles, np.int32) | numpy.array |
# Licensed under an MIT open source license - see LICENSE
"""
SCOUSE - Semi-automated multi-COmponent Universal Spectral-line fitting Engine
Copyright (c) 2016-2018 <NAME>
CONTACT: <EMAIL>
"""
import numpy as np
import sys
import warnings
import pyspeckit
import matplotlib.pyplot as plt
import itertools
import time... | np.abs(velolist[i] - adjacent_velocity) | numpy.abs |
import math
import numpy as np
from scipy import signal
def gaussian_pdf_1d(mu, sigma, length):
'''Generate one dimension Gaussian distribution
- input mu: the mean of pdf
- input sigma: the standard derivation of pdf
- input length: the size of pdf
- output: a row vector represent... | np.arctan(ly/lx) | numpy.arctan |
"""
desisim.spec_qa.redshifts
=========================
Module to run high_level QA on a given DESI run
Written by JXP on 3 Sep 2015
"""
from __future__ import print_function, absolute_import, division
import matplotlib
# matplotlib.use('Agg')
import numpy as np
import sys, os, pdb, glob
from matplotlib import pyp... | np.max(xval) | numpy.max |
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